In this article, we evaluate the performance of the T2 chart based on the principal components (PC chart) and the simultaneous univariate control charts based on the original variables (SU X̄ charts) or based on the principal components (SUPC charts). The main reason to consider the PC chart lies on the dimensionality reduction. However, depending on the disturbance and on the way the original variables are related, the chart is very slow in signaling, except when all variables are negatively correlated and the principal component is wisely selected. Comparing the SU X̄, the SUPC and the T 2 charts we conclude that the SU X̄ charts (SUPC charts) have a better overall performance when the variables are positively (negatively) correlated. We also develop the expression to obtain the power of two S 2 charts designed for monitoring the covariance matrix. These joint S2 charts are, in the majority of the cases, more efficient than the generalized variance |S| chart.
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机译：在本文中，我们基于主成分（PC图表）和基于原始变量（SUX̄图表）或基于主成分（SUPC图表）的同时单变量控制图评估T2图表的性能。考虑PC图表的主要原因在于降维。但是，根据扰动和原始变量的关联方式，图表会非常缓慢地发出信号，除非所有变量均呈负相关且明智地选择了主要成分。比较SUX̄，SUPC和T 2图，我们得出结论，当变量呈正（负）相关时，SUX̄图（SUPC图）具有更好的总体性能。我们还开发了表达式，以获得两个S 2图表的功效，这些图表设计用于监视协方差矩阵。在大多数情况下，这些联合S2图比广义方差| S |更有效。图表。
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